Course syllabus

Jump to today

Course memo

The course is offered by the department of Mechanics and Maritime Sciences.

General information

The course will be given only on-site at Chalmers. There will be no remote (Zoom) lectures. More information will follow in the first lecture.

Contact details

During the course, we strive to be available as much as possible. You are welcome to ask questions at any time, e.g. in the lectures (and you may also ask questions via e-mail or telephone). You are always welcome at our offices. You do not need to make an appointment, but since we are not always in our offices it's a good idea to first check that we are there (e.g. via e-mail or telephone).

Lecturer and examiner:

Professor Mattias Wahde, tel.: 031 772 3727, e-mail: mattias.wahde@chalmers.se

Course assistant:

Minerva Suvanto, e-mail: minerva.suvanto@chalmers.se

Finding our offices: Go to Hörsalsvägen 7, enter the building (nya M-huset), so that you have Café Bulten on your right as you enter. Then go up one flight of stairs, and enter the corridor (Vehicle Engineering and Autonomous Systems). If the door is locked, please dial the appropriate number, as shown in the list beside the door.

Course purpose

The aim of the course is for the participants to gain knowledge regarding natural language processing (NLP) and conversational AI. Students will learn about text processing, text classification, large language models (LLMs), and conversational agents, both chatbots and task-oriented agents. Emphasizing interpretable and accountable AI, the course covers both theoretical aspects and the practical aspects related to NLP and conversational AI. Ethical aspects of conversational AI are also covered.

Schedule

The course schedule is given below. The lectures can also be found in TimeEdit.

Date Room Time  Content
20240116 HC3 08.00-09.45 Course introduction and motivation; Brief description of the topics covered in the course; Introduction to conversational AI.
20240117 HA3 13.15-17.00

Text preprocessing (I) (human language and grammar, tokenization, error correction and normalization). Introduction to C# and Python (Colab and Jupyter)
20240123 HC3 08.00-09.45 Text preprocessing (II) (part-of-speech tagging, named-entity recognition). 
20240124 HA3 13.15-17.00 Text classification (classification vs. regression, perceptrons, Bayesian methods, kNN classification), advanced C# programming
20240130 HC3 08.00-09.45 Statistical language models (I),  (n-gram models, performance evaluation)
Handout of Assignment 1.
20240131 HA3 13.15-17.00

Statistical language models (II), (embeddings, sequence-to-sequence learning, transformers, LLMs (e.g. chatGPT), text classification with DNNs, )
20240206 HC3 08.00-09.45 Interpretability and accountability in NLP. Handout of Assignment 2.
20240207 --- --- No lecture
20240213 HC3 08.00-09.45 Chatbots
20240214 HA3 13.15-17.00 Assignment work session (lecturer and assistant available as tutors in the classroom)
20240220 HC3 08.00-09.45 Task-oriented agents; DAISY (task-oriented dialogue manager).
20240221 HA3 13.15-17.00 AI ethics (1 hour), assignment work session (3 hours). Handin of Assignment 1.
20240227 --- --- No lecture  (work on assignments)
20240228 --- ---

No lecture (work on assignments)
20240305 HC3 08.00-09.45

Assignment work session (lecturer and assistant available as tutors in the classroom)
20240306 HA2 13.15-17.00

Course summary, assignment work session. Handin of Assignment 2.

Course literature

The course literature will consist of a compendium, lecture notes (slides), and links to various scientific papers and web resources. This material will be provided gradually during the course. Note that, due to the large changes in the course (resulting from the rapid development in the field), the compendium from previous years is now outdated. All course material will be provided (free of charge) on the Modules page.

Changes made since the last occasion (2023)

The course has been modified quite a bit, mainly reflecting the rapid development in the field of large language models (LLMs). Moreover, part-of-speech tagging and named-entity recognition have been added. 

Learning outcomes

After completion of the course the student should be able to...

  • Understand and implement text preprocessing.
  • Understand and use statistical language models.
  • Carry out text classification with several different methods.
  • Implement and use chatbots.
  • Describe and contrast chatbots and task-oriented agents.
  • Describe and compare various different (applications of) intelligent agents.
  • Describe and discuss interpretable (conversational) AI, and be able to contrast interpretable models with black box models.
  • Describe and discuss the ethical implications of conversational agents.

Examination 

Examination: There will be two assignments, worth a total of 100 p. More details will follow when the course starts.

The dates for handing in the assignments can be found in the schedule above. The detailed requirements will be given in connection with each assignment. In order to pass the course, students must hand in satisfactory solutions to all four assignments. Grades will then be set as follows:

Grade 3: Up to 60p

Grade 4: [61, 80] p

Grade 5: [81-100] p

For GU students, the following applies:

Grade G: Up to 73p

Grade VG: 74p and above

Note: All deadlines are at 23.59.59 on the specified dates, and it is the time when the submission is received that counts. Submissions that are handed in late will receive a lower score, as described in each assignment document. The possibility of submitting assignments will be closed at midnight on 20240318.

Re-examination, grade improvement etc.: It is possible for students to improve their grade by resubmitting assignments. However, after the final submission deadline during the course (20240318) resubmission is only allowed in connection with the re-exam periods in August (2024) and January (2025). 

Note: All submissions must be handed in via the Canvas page. E-mailed submissions will not be considered.

Course summary:

Date Details Due